1. Field of the Invention
The present invention generally relates to automated inspection systems for identifying geometric characteristics of objects and, more particularly, the present invention relates to orientation systems for use with automated inspection systems for inspecting, sorting and other handling of parts and components which require identification of their geometric characteristics.
2. State of the Art
In automated assembly operations performed by automated assembler machines such as robotic devices, it is critical that component parts which are supplied to an assembly line be non-defective. Further, it is often important that component parts be supplied to an assembly line in particular spatial orientations. One reason for such requirements is that conventional assembler machines lack sufficient dexterity and artificial intelligence to handle component parts which are presented in other than predetermined geometrical orientations. Another reason for such requirements is that conventional assembler machines lack the ability to distinguish defective or non-conforming components from acceptable ones; accordingly, if an automated assembler machine receives a non-conforming object for assembly, the machine may attempt to mount the object into the workpiece assembly regardless of whether the object is suited for the assembly. Thus if non-conforming, defective or misoriented component parts are presented to conventional automated assembler machines, there is substantial risk of disruption of assembly line operations, damage to equipment on the line, and damage to products being assembled.
To minimize the number of defective and non-conforming components which reach automated assembler machines, a typical approach is to impose quality control standards upon vendors and suppliers of the components, with the ideal result being summarized in the phrase "zero defects". Quality control measures that approach that ideal, however, involve expense and may increase the cost of components and the final product incorporating the components. Further, strict quality control at the vendor or supplier level usually does not eliminate the necessity for inspection of component parts prior to assembly. Thus, manufacturers typically must inspect all components, including high quality components prior to their being conveyed to automated assembly lines. In many factories, such inspection is performed by human inspectors; however, many inspection tasks can be difficult and tedious for humans to perform, resulting in high rates of error. Also, labor costs for human inspectors may be substantial.
To improve the effectiveness of inspections and to reduce manufacturing costs, automated inspection systems have been provided. Some automated inspection systems utilize machine vision to inspect objects, particularly small component parts. Such systems typically employ optical equipment that receives light reflected from objects during inspection. Although some success has been achieved with optical systems, such systems usually require substantial capacity for signal processing and computing in order to organize even simple geometrical configurations.
Automated inspection systems have also been suggested to provide recognition of geometric characteristics of small objects based upon transmission and reception of reflected sonic waves, thereby providing "acoustic signature" of the objects. In this regard, attention is drawn to U.S. Pat. Nos. 4,095,474; 4,200,921; 4,287,769; and 4,576,286 to the inventor Bruce S. Buckley, herein. Such systems have been successfully demonstrated and, as compared to optical systems, have been shown to require less capacity for signal processing and computation.
Still other automated inspection systems provide recognition of objects by establishing electromagnetic fields which interact with the objects. Such systems then operate to sense changes in the electromagnetic fields to obtain an "electromagnetic signature" of inspected objects. Systems of this type have been proposed using eddy currents and capacitive sensors to establish the electromagnetic fields.
In operation of automated inspection systems, such as described above, two types of errors may arise. The first type of error is usually called "false acceptance" These errors result from accepting articles which are defective or otherwise do not conform to predetermined standards. For example, in an automated inspection system for inspecting bolts, a false acceptance error would arise if a bolt with a damaged head were to pass the inspection station without rejection. By way of further example, a false acceptance error would arise if a screw, although non-defective, were to pass on inspection station which was designed to pass only non-defective bolts. False acceptance errors can severely affect automated assembly operations and thus can be quite costly and time consuming. Accordingly, workers in the art have made substantial efforts to avoid such errors, usually by adjusting automated inspection devices to prescribe narrow tolerances for objects which are judged to be acceptable. For example, in automated inspection equipment of the type based upon detection of reflected waves, false acceptance errors may be minimized by accepting only objects which produce reflected waves with narrowly defined characteristics. Likewise in inspection systems which based upon detection of changes in electromagnetic fields, false acceptance errors may be minimized by narrowly defining acceptable field changes.
Another type of error which can arise in inspection systems is the error of rejecting objects which are not defective but which, in fact, conform to predetermined standards. Such errors are usually called "false rejections". Although false rejection errors may have less serious immediate consequences than false acceptance errors, nevertheless problems can arise if rejection rates for conforming objects are high. For example, in inspection systems where objects are presented for inspection in one-by-one series, erroneous rejection of conforming components can delay assembly operations. To alleviate the effects of false rejection errors, inspection systems have been devised where all rejected objects are resubmitted though the inspection equipment; however, even such systems can be overwhelmed by high rates of false rejections.
In automated inspection systems which recognize geometrical characteristics of inspected objects, false rejection and false acceptance errors are often related to the orientation in which objects are presented for inspection. Thus, if conforming objects are presented for inspection in unusual orientations, the probability of improper rejections usually increases. Likewise, if objects are submitted for inspection while moving in a manner different than previously inspected objects, the probability of improper rejection usually increases. Types of motions which can cause false rejection errors include, for example, oscillating movement where objects teeter rapidly from one position to another.
In automated inspection equipment that operates upon the principal of detection of fields and reflected waves, it is known that statistical methods can be used to process analog electrical signals derived from the fields and reflected waves. In operation of such equipment, inspected objects are deemed acceptable only if the signals derived from inspection of the objects fall within predetermined statistical ranges, usually expressed in terms of standard deviations or variances. The statistical measures in such systems have been found to vary depending upon the orientation or motions of objects undergoing inspection; if objects are inspected while in unusual orientations or while moving in manners different than most other inspected objects, the derived statistical measures will be relatively inexact. Thus, in inspection systems which sense fields and reflected waves, unusual orientations or motion of objects during inspection should be minimized.
At this juncture, it can be appreciated that automated inspection systems can be used for purposes other than those mentioned above. For example, automated inspection systems can be used to sort objects. An example of a sorting task would be to sort nuts from bolts. In that operation, if an automated inspection system received both nuts and bolts, the inspection equipment could sort nuts from bolts by rejecting nuts on the basis that they are non-conforming bolts. When automated inspection machinery is used to accomplish sorting tasks, false acceptance errors and false rejection errors can result in unintended mixing of objects of one kind with those of another kind.
Automated inspection systems can also be utilized to improve the quality of manufacturing operations related to the inspected objects. Thus, if an automated inspection system provides information that particular dimensions of inspected objects are out of tolerance, that information can be used to adjust manufacturing machines and tools to bring the manufactured objects back into tolerance. In this function, too, unusual orientations or motions of objects during inspection can cause false rejection and false acceptance errors and, thus, can adversely affect manufacturing operations.
Further, automated inspection equipment may be utilized to assure that objects are conveyed in particular orientations. In such a case, the inspection equipment would operate to reject objects which were misoriented. Here again, false rejection errors and false acceptance errors can adversely affect operations.
It is known that automated inspection systems can be programmed to identify conforming objects by processes which may be called learning processes. In essence, such learning processes depend upon submitting objects for inspection which are known to be acceptable and then performing certain statistical computations upon signals derived from waves reflected from the objects or fields associated with the objects. We have found that the effectiveness of such learning processes, especially in terms of minimizing false acceptance errors and false rejection errors, can be improved to the extent that signals associated with acceptable objects have relatively small variances or standard deviations. Also, we have found that such statistical measures depend upon the stability of orientation of objects presented for inspection. For example, if objects are in unusual positions or are oscillating during the period of a learning process, the ranges of the computed statistical measures will increase and this will, in turn, increase the likelihood of false acceptance and false rejection errors.